Deepmal-deep learning models for malware traffic detection and classification
G Marín, P Caasas, G Capdehourat - … of the 3rd international data science …, 2021 - Springer
Robust network security systems are essential to prevent and mitigate the harming effects of
the ever-growing occurrence of network attacks. In recent years, machine learning-based …
the ever-growing occurrence of network attacks. In recent years, machine learning-based …
Deep in the dark-deep learning-based malware traffic detection without expert knowledge
G Marín, P Casas… - 2019 IEEE Security and …, 2019 - ieeexplore.ieee.org
With the ever-growing occurrence of networking attacks, robust network security systems are
essential to prevent and mitigate their harming effects. In recent years, machine learning …
essential to prevent and mitigate their harming effects. In recent years, machine learning …
NetML: A challenge for network traffic analytics
Classifying network traffic is the basis for important network applications. Prior research in
this area has faced challenges on the availability of representative datasets, and many of the …
this area has faced challenges on the availability of representative datasets, and many of the …
Cyber attack detection thanks to machine learning algorithms
A Delplace, S Hermoso, K Anandita - arXiv preprint arXiv:2001.06309, 2020 - arxiv.org
Cybersecurity attacks are growing both in frequency and sophistication over the years. This
increasing sophistication and complexity call for more advancement and continuous …
increasing sophistication and complexity call for more advancement and continuous …
When a RF beats a CNN and GRU, together—A comparison of deep learning and classical machine learning approaches for encrypted malware traffic classification
Internet traffic classification plays a crucial role in Quality of Experience (QoE), Quality of
Services (QoS), intrusion detection, and traffic-trend analyses. While there is no theoretical …
Services (QoS), intrusion detection, and traffic-trend analyses. While there is no theoretical …
Malphase: Fine-grained malware detection using network flow data
M Piskozub, F De Gaspari, F Barr-Smith… - Proceedings of the …, 2021 - dl.acm.org
Economic incentives encourage malware authors to constantly develop new, increasingly
complex malware to steal sensitive data or blackmail individuals and companies into paying …
complex malware to steal sensitive data or blackmail individuals and companies into paying …
An LSTM-based deep learning approach for classifying malicious traffic at the packet level
Recently, deep learning has been successfully applied to network security assessments and
intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional …
intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional …
Uncovering APT malware traffic using deep learning combined with time sequence and association analysis
W Niu, J Zhou, Y Zhao, X Zhang, Y Peng, C Huang - Computers & Security, 2022 - Elsevier
Traditional malware detection methods based on static traffic characteristics and machine
learning are hard to cope with the increasing number of APT malware variants. In order to …
learning are hard to cope with the increasing number of APT malware variants. In order to …
IoT malware network traffic classification using visual representation and deep learning
With the increase of IoT devices and technologies coming into service, Malware has risen as
a challenging threat with increased infection rates and levels of sophistication. Without …
a challenging threat with increased infection rates and levels of sophistication. Without …
Multi-task hierarchical learning based network traffic analytics
Classifying network traffic is the basis for important network applications. Prior research in
this area has faced challenges on the availability of representative datasets, and many of the …
this area has faced challenges on the availability of representative datasets, and many of the …